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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

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Volume 8 | Issue 9

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Paper Title:	EARLY DIAGNOSIS AND PREDICTION OF FETAL ABNORMALITIES USING MACHINE LEARNING
Authors Name:	A. VIJAY , P. SUMANTH , D. NITHIN ARAVINDH , DR. G. ROSLINE NESAKUMARI
Unique Id:	IJSDR2305068
Published In:	Volume 8 Issue 5, May-2023
Abstract:	Normal fetal growth is a critical component of a healthy pregnancy and influences the long-term health of the offspring. However, defining normal and abnormal fetal growth has been a long-standing challenge in clinical practice and research. The authors review various references and standards that are widely used to evaluate fetal growth, and discuss common pitfalls of current definitions of abnormal fetal growth. Pros and cons of different approaches to customize fetal growth standards are described. The authors further discuss recent advances towards an integrated definition for fetal growth restriction. Such a definition may incorporate fetal size with the status of placental health measured by maternal and fetal Doppler velocimetry and biomarkers, biophysical findings and genetics. Although the concept of an integrated definition appears promising, further development and testing are required. An improved definition of abnormal fetal growth should benefit both research and clinical practice.
Keywords:	fetal growth, risks in pregnancy, abnormal fetal size,health of offspring
Cite Article:	"EARLY DIAGNOSIS AND PREDICTION OF FETAL ABNORMALITIES USING MACHINE LEARNING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.480 - 505, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305068.pdf
Downloads:	000251436
Publication Details:	Published Paper ID: IJSDR2305068 Registration ID:206116 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: 480 - 505 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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